Sentence examples for multivariate normality tests from inspiring English sources

Normality tests, i.e., univariate and multivariate normality tests, with reference to the values of skewness and kurtosis of the observed variables, were conducted in this study to test the assumption of normality in SEM.

In practice, the observed variables may often reveal some significant p-values for both kurtosis and skewness, in both univariate and multivariate normality tests, which could suggest a potential violation of normality.

Variables with a skewed distribution were transformed using quadratic or Log transformations and multivariate normality was tested using Henze-Zirkler's test [56].

They pose, however, some limitations derived from (i) potential trait multicollinearity, (ii) biases introduced by failure to include traits that covary with fitness, (iii) deviations of response variables from multivariate normality assumed for hypothesis testing, and (iv) failure to consider environmental factors that may induce spurious correlations [ 9].

The IC algorithm is based on conditional independencies tests, such that under multivariate normality, it can be implemented by using partial correlations tests.

In the test of multivariate normality for continuous variables, the measure of relative multivariate kurtosis as calculated by PRELIS equaled 1.097.

A test of multivariate normality was used to determine the skewness of data to be used during CFA (41).

Although the error terms failed the Shapiro‐Wilk test for multivariate normality (p= 2.8∗10−4 for temozolomide and p= 1.4∗10−8 for 5‐fluorouracil, [ 18]), the goal of this simulation was to use real data as a guide in simulation.

Multivariate normality is also reasonable when testing the means of samples over many conditions, and when proper data transformation is used on the *-seq data, such as log-transformation, averaging signals over intervals, or quantile transformation to z-scores.

The distribution of data was assessed regarding univariate and multivariate normality (Kolmogorov-Smirnov and Mardia's tests) and described and analyzed accordingly.

The univariate normality assumption was checked with the one-sample Kolmogorov-Smirnov Test (SPSS) and multivariate normality was assessed with the Shapiro-Wilk goodness-of-fit test (using JMP Pro 11) on the distribution of the Mahalanobis distances.